Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system

Expert Systems with Applications

Volumn 37, Issue 6, 2010, Pages 4413-4424

  Chou, Pao Hua ^a   Wu, Menq Jiun ^a   Chen, Kuang Ku ^a  

^a NATIONAL CHANGHUA UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

Genetic algorithm; Support vector machine; Virtual metrology

Indexed keywords

ABNORMAL CONDITIONS; ARTIFICIAL INTELLIGENT; BACK-PROPAGATION NEURAL NETWORKS; EMPIRICAL RESULTS; GENETIC ALGORITHM SUPPORT VECTOR MACHINES; GLOBAL MARKET; GLOBAL MARKET SHARE; HIGH-TECH INDUSTRIES; INCREMENTAL LEARNING; KERNEL FUNCTION; MANUFACTURING PROCESS; MANUFACTURING SYSTEM; NOVELTY DETECTION; ONLINE QUALITY; PERFORMANCE DEGRADATION; PREDICTION ACCURACY; PREDICTIVE MAINTENANCE POLICY; PROCESS PARAMETERS; PROCESSING QUALITY; PRODUCTION CAPACITY; PRODUCTION EQUIPMENTS; PRODUCTION SCALE; RADIAL BASIS FUNCTION NEURAL NETWORKS; REAL-TIME QUALITY; SEMICONDUCTOR MANUFACTURING; STABILITY OF PROCESS; SUPPORT VECTOR DATA DESCRIPTION; THIN FILM TRANSISTOR LIQUID CRYSTAL DISPLAYS; VIRTUAL METROLOGY; WAFER QUALITY;

COMPETITION; GENETIC ALGORITHMS; IMAGE RETRIEVAL; IMAGE SEGMENTATION; INDUSTRIAL ENGINEERING; INTERNATIONAL TRADE; LEARNING ALGORITHMS; LIQUID CRYSTAL DISPLAYS; LIQUID CRYSTALS; MANUFACTURE; MATHEMATICAL MODELS; NANOELECTRONICS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; PRODUCTION ENGINEERING; RADIAL BASIS FUNCTION NETWORKS; SILICON WAFERS; STATISTICAL PROCESS CONTROL; SUPPORT VECTOR MACHINES; THIN FILM TRANSISTORS; VECTORS;

SUBSTRATES;

EID: 77249166490     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.11.087     Document Type: Article

References (39)

1. Bhatikar S.R., and Mahajan R.L. Artificial neural-network-based diagnosis of CVD barrel reactor. IEEE Transactions on Semiconductor Manufacturing 15 1 (2002) 71-78
2. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 121-167
3. Chang, Y. C., & Cheng, F. T. (2005). Application development of virtual metrology in semiconductor industry. In Proceedings of the 31st annual conference on IEEE industrial electronics society, Raleigh, NC (pp. 124-129).
4. Chen W.C., Lee H.I., Deng W.J., and Liu K.Y. The implementation of neural network for semiconductor PECVD process. Expert Systems with Applications 32 (2007) 1148-1153
5. Chen, Y. T., Yang, H. C., & Cheng, F. T. (2006). Multivariate simulation assessment for virtual metrology. In Proceedings of the 2006 IEEE international conference on robotics and automation, Orlando, FL (pp. 1048-1053).
6. Corinna C., and Vapnik V. Support-vector network. Machine Learning 20 (1995) 273-297
7. Davis L. Handbook of genetic algorithms (1991), Van Nostrand Reinhold, Amsterdam
8. Diebold, A. C. (1995). Overview of metrology requirements based on the 1994 national technology roadmap for semiconductor. In Proceedings of the IEEE/SEMI advanced semiconductor manufacturing conference and workshop (pp. 50-60).
9. Dumais, S., & Chen, H. (2000). Hierarchical classification of Web content. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval, Athens, Greece (pp. 256-263).
10. Fan Z.P., Chen Y., Ma J., and Zhu Y. Decision support for proposal grouping: A hybrid approach using knowledge rule and genetic algorithm. Expert Systems with Applications 36 (2009) 1004-1013
11. Fröhlich, H., & Chapelle, O. (2003). Feature selection for support vector machines by means of genetic algorithms. In Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento, California, USA (pp. 142-148).
12. Gan G., Wu J., and Yang Z. Genetic fuzzy k-modes algorithm for clustering categorical data. Expert Systems with Applications 36 (2009) 1615-1620
13. Giarratano J., and Riley G. Expert systems-principle and programming. 3rd ed. (1998), PWS Publishing Company, Boston, MA
14. Goldberg D.E. Genetic algorithms in search optimization and machine learning (1989), Addison-Wesley, MA
15. Guyon I., Weston J., Barnhill S., and Bapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 46 1-3 (2002) 389-422
16. Himmel, C. D., Kim, B., & May, G. S. (1992). A comparison of statistically based and neural network models of plasma etch behavior. In Proceedings of the 4th international semiconductor manufacturing science symposium (pp. 124-129).
17. Himmel C.D., and May G.S. Advantages of plasma etch modeling using neural networks over statistical techniques. IEEE Transactions on Semiconductor Manufacturing 6 2 (1993) 103-111
18. Holland J.H. Adaptation in natural and artificial systems (1975), The University of Michigan Press, Ann Arbor MI
19. Hsieh K.L., and Tong L.I. Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks. Computers in Industry 46 (2001) 1-12
20. Huang C.L., Chen M.C., and Wang C.J. Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33 (2007) 847-856
21. Kim B., and Hong W.S. Use of neural network to characterize a low pressure temperature effect on refractive property of silicon nitride film deposited by PECVD. IEEE Transactions on Plasma Science 32 1 (2004) 84-89
22. Kim B., Kim D.W., and Park G.T. Prediction of plasma etching using a polynomial neural network. IEEE Transactions on Plasma Science 31 6 (2003) 1330-1336
23. Kovalerchuk B., and Vityaev E. Data mining in finance (2000), Kluwer
24. Lee S.F., and Spanos C.J. Prediction of wafer state after plasma processing using real-time equipment data. IEEE Transactions on Semiconductor Manufacturing 8 3 (1995) 252-261
25. Limanond S., Si J., and Tsakalis K. Monitoring and control of semiconductor manufacturing processes. IEEE Control Systems Magazine 18 6 (1998) 46-58
26. Lin, T. H., Hung, M. H., Lin, R. C., & Cheng, F. T. (2006). A virtual metrology scheme for predicting CVD thickness in semiconductor manufacturing. In Proceedings of the 2006 IEEE international conference on robotics and automation, Orlando, Florida (pp. 1054-1059).
27. Mao K.Z. Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Transactions on Systems, Man, and Cybernetics 34 1 (2004) 60-67
28. McIvor R.T., McCloskey A.G., Humphreys P.K., and Maguire L.P. Using a fuzzy approach to support financial analysis in the corporate acquisition process. Expert Systems with Applications 27 (2004) 533-547
29. Mitchell M. An introduction to genetic algorithms (1996), MIT Press/Addison-Wesley, Cambridge, MA
30. Raymer M.L., Punch W.F., Goodman E.D., Kuhn L.A., and Jain A.K. Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation 4 2 (2000) 164-171
31. Salcedo-Sanz, S., Prado-Cumplido, M., Pe'rez-Cruz, F., & Bousonõ-Calzo'n, C. (2002). Feature selection via genetic optimization. In Proceedings of the ICANN international conference on artificial neural networks, Madrid, Spain (pp. 547-552).
32. Stokes D., and May G. Real-time control of reactive ion etching using neural networks. IEEE Transactions on Semiconductor Manufacturing 13 4 (2000) 469-480
33. Su Y.C., Hung M.H., Cheng F.T., and Chen Y.T. A processing quality prognostics scheme for plasma sputtering in TFT-LCD manufacturing. IEEE Transactions on Semiconductor Manufacturing 19 2 (2006) 183-194
34. Tang K.S., Man K.F., Kwong S., and He Q. Genetic algorithms and their applications. IEEE Signal Processing Magazine 13 (1996) 22-37
35. Vapnik V. The nature of statistical learning theory (1995), Springer-Verlag, New York
36. Yi J., Sheng Y., and Xu C.S. Neural network based uniformity profile control of linear chemical-mechanical planarization. IEEE Transactions on Semiconductor Manufacturing 16 4 (2003) 609-620
37. Wang Y.F. Mining stock price using fuzzy rough set system. Expert Systems with Applications 24 (2003) 13-23
38. Weston J., Mukherjee S., Chapelle O., Pontil M., Poggio T., and Vapnik V. Feature selection for SVM. In: Solla S.A., Leen T.K., and Muller K.-R. (Eds). Advances in neural information processing systems Vol. 13 (2001), MIT Press, Cambridge, MA 668-674
39. Zeng, X. P., Li, Y. M., & Qin, J. (2008). A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection. Neurocomputing. Available online 6 March 2008.